Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. An in-store customer traffic analysis system, comprising: a sensor network comprising a plurality of biometric sensors positioned within a retail store, the plurality of sensors comprising a first sensor; a plurality of displays comprising a first display; one or more memory devices storing instructions; and one or more hardware processors configured to execute the instructions to: receive, over an electronic communications network, a first sensor signal indicating that a first user has been recognized by the first sensor; extract a first user identifier from the first sensor signal; correlate the first sensor signal to the first display; generate a first foot traffic record associated with the first user identifier and the first display, based on the first sensor signal; store the first foot traffic record; determine a first user demographic group associated with the first user; obtain, based on an identifier of the first display, demographic-display data associated with the determined user demographic, the demographic-display data indicating an interest correlation between the first display and the user demographic in a time window; determine a recommendation to be displayed in the first display based on the demographic-display data; generate a processor-executable instruction to modify directional signage of the first display towards a product in the retail store according to the recommendation in an automated fashion; calculate a first score based on an amount of time the first user spends within a proximity of the modified first display and a direction from which the first user approached the first display; associate the first score with the first user identifier; store the associated score; and update, based on the amount of time the first user spends within a proximity of the modified first display, a second score indicating an interest of members of a second user demographic group associated with the first user in the first display in a time slot of a particular day.
This system analyzes customer traffic in retail stores using biometric sensors and displays to optimize product recommendations. The system includes a network of biometric sensors placed throughout a retail store, multiple displays, memory devices, and processors. When a user is detected by a sensor, the system identifies the user, correlates the detection to a nearby display, and generates a foot traffic record. The system then determines the user's demographic group and retrieves demographic-display data, which indicates how users in that demographic interact with the display. Based on this data, the system generates a recommendation and automatically adjusts the display's directional signage to guide the user toward a relevant product. The system calculates a score based on the user's interaction time and approach direction, storing this data for future analysis. Additionally, the system updates a demographic-specific score to track interest in the display during specific time slots. This approach enhances targeted marketing by dynamically adapting displays to user demographics and behavior patterns.
2. The system of claim 1 , wherein the first sensor senses at least one of a face of the first user, a fingerprint of the first user, or a voice of the first user.
A biometric authentication system verifies user identity using multiple sensors to enhance security. The system includes at least one sensor that captures biometric data from a user, such as facial features, fingerprints, or voice patterns. This data is processed to authenticate the user's identity, ensuring secure access to devices or systems. The system may also include additional sensors or components to further validate the user, such as a second sensor for capturing biometric data from a second user. The authentication process involves comparing the captured biometric data against stored templates to determine a match, allowing or denying access based on the verification result. The system is designed to improve security by reducing the risk of unauthorized access through multi-factor biometric authentication. The use of different biometric modalities, such as facial recognition, fingerprint scanning, or voice recognition, provides flexibility and robustness in user verification. The system may be integrated into various devices or applications where secure authentication is required, such as smartphones, computers, or access control systems. The primary problem addressed is the need for reliable and secure user authentication methods that are resistant to spoofing and unauthorized access attempts.
3. The system of claim 1 , wherein the one or more processors are further configured to execute instructions to; obtain a first user profile corresponding to the first user; identify a demographic category based on the first user profile; generate a display-demographic map indicating a correlation between the first display and the demographic category; and store the display-demographic map.
This invention relates to a system for analyzing and correlating user demographics with digital display content. The system addresses the challenge of understanding how different demographic groups interact with digital displays, such as advertisements or media content, to improve targeting and personalization. The system includes one or more processors configured to obtain a user profile for a first user, which may contain demographic information such as age, gender, location, or interests. The processors then identify a demographic category from this profile, such as "young adults" or "urban professionals." Next, the system generates a display-demographic map that correlates a specific digital display (e.g., an advertisement or video) with the identified demographic category, indicating how likely that demographic is to engage with the content. This map is then stored for future reference, enabling data-driven decisions in content targeting and optimization. The system may also include additional components, such as a display module to present the digital content and a user interface for managing the demographic data. The display-demographic map can be used to refine advertising strategies, improve user engagement, or tailor content recommendations based on demographic insights. The invention enhances digital marketing and media delivery by providing actionable data on how different demographic groups respond to specific displays.
4. The system of claim 3 , wherein the one or more processors are further configured to execute instructions to: obtain an inventory listing associated with the retail store; and generate a recommendation for a product contained in the inventory listing, based on the display-demographic map.
This invention relates to a retail analytics system that uses demographic data to optimize product placement and recommendations. The system addresses the challenge of efficiently matching products to customer demographics in physical retail stores, improving sales and customer satisfaction. The system includes a display-demographic map that correlates product displays with demographic data, such as age, gender, or purchasing behavior, collected from customer interactions. The system also obtains an inventory listing from the retail store, which includes available products and their locations. Using the display-demographic map, the system generates recommendations for products that align with the demographic profiles of customers likely to visit specific store sections. These recommendations help retailers strategically place products to maximize visibility and appeal to target demographics. The system may also adjust recommendations based on real-time data, such as foot traffic patterns or seasonal trends, to further optimize product placement. The goal is to enhance in-store customer experience and increase sales by leveraging demographic insights.
5. The system of claim 4 , wherein the one or more processors are further configured to execute instructions to: provide the recommendation for the product contained in the inventory listing via a graphical user interface.
This invention relates to a system for recommending products from an inventory listing to users via a graphical user interface. The system addresses the challenge of efficiently suggesting relevant products to users, improving user engagement and sales conversion. The system includes one or more processors configured to analyze user data, such as browsing history, purchase history, or preferences, to generate personalized product recommendations. These recommendations are then displayed to the user through a graphical user interface, enhancing the user experience by presenting tailored product suggestions. The system may also incorporate additional features, such as filtering recommendations based on inventory availability or user-specific criteria, to further refine the suggestions. By dynamically adapting recommendations to individual user behavior, the system aims to increase the likelihood of successful product discovery and purchase. The graphical user interface ensures that recommendations are presented in an intuitive and visually appealing manner, facilitating seamless interaction between the user and the system. This approach optimizes the recommendation process, making it more efficient and user-friendly.
6. The system of claim 5 , wherein the one or more processors are further configured to execute instructions to: obtain a user input via the graphical user interface in response to providing the recommendation for the product contained in the inventory listing; generate a display instruction based on the obtained user input; and provide the generated display instruction via the graphical user interface.
This invention relates to a system for enhancing user interaction with product recommendations in an inventory management interface. The system addresses the problem of inefficient user engagement with recommended products, particularly in scenarios where users need to quickly access and act on product suggestions within an inventory listing. The system includes a graphical user interface (GUI) that displays an inventory listing of products. One or more processors are configured to analyze the inventory data to identify and recommend products that match specific criteria, such as user preferences, inventory availability, or sales trends. The system then presents these recommendations within the GUI, allowing users to interact with them directly. Upon receiving a user input in response to a product recommendation, the system processes the input to generate a display instruction. This instruction may modify the GUI to highlight the recommended product, update its status, or trigger additional actions like adding the product to a cart or initiating a purchase. The system then executes the display instruction, ensuring a seamless and responsive user experience. The invention improves efficiency by reducing the steps required for users to act on recommendations, thereby streamlining inventory management and decision-making processes. The system dynamically adapts to user interactions, enhancing usability and productivity in inventory-related workflows.
7. The system of claim 1 , wherein the one or more processors are further configured to execute instructions to: receive a second sensor signal indicating that a second user has been recognized by the first sensor; extract a second user identifier from the second sensor signal; correlate the second sensor signal to the display; generate a second foot traffic record associated with the second user identifier and the display, based on the second sensor signal; and store the second generated foot traffic record, generate a recommendation for a product identified in the retail inventory listing based on a display-demographic map, wherein the display-demographic map is based on a first user profile related to the first user and a second user profile related to the second user.
This invention relates to a retail analytics system that tracks user interactions with displays and generates product recommendations based on demographic data. The system addresses the challenge of understanding customer behavior near retail displays to improve targeted marketing and inventory management. The system includes one or more processors configured to receive sensor signals from a first sensor detecting a first user near a retail display. The system extracts a first user identifier from the sensor signal, correlates the signal to the display, and generates a foot traffic record associating the user identifier with the display. This record is stored for analysis. The system also receives a second sensor signal indicating a second user has been recognized by the first sensor, extracts a second user identifier, correlates the signal to the display, and generates a second foot traffic record. The system then generates a product recommendation based on a display-demographic map, which is derived from user profiles associated with the first and second users. The recommendation is for a product listed in the retail inventory and is tailored to the demographic characteristics of users interacting with the display. The system enhances retail analytics by linking user demographics to display interactions, enabling more personalized and effective product recommendations.
8. A method for in-store customer traffic analysis, the method comprising; receiving, over an electronic communications network, a first sensor signal indicating that a first user has been recognized by a first biometric sensor of a sensor network in a retail store, the first sensor being associated with a first display in the retail store; extracting, by one or more processors, a first user identifier from the first sensor signal; correlating, by the one or more processors, the first sensor signal to the first display; generating, by the one or more processors, a first foot traffic record associated with the first user identifier and the first display, based on the first sensor signal; storing the first foot traffic record; determining a first user demographic group associated with the first user; obtaining, based on an identifier of the first display, demographic-display data associated with the determined user demographic, the demographic-display data indicating an interest correlation between the first display and the user demographic in a time window; determining a recommendation to be displayed in the first display based on the demographic-display data; generating a processor-executable instruction to directional signage of the first display towards a product in the retail store according to the recommendation in an automated fashion; calculating a first score based on an amount of time the first user spends within a proximity of the modified first display; associating the first score with the first user identifier and a direction from which the first user approached the display; storing the associated score; and updating, based on the amount of time the first user spends within a proximity of the modified first display, a second score indicating an interest of members of a second user demographic group associated with the first user in the first display in a time slot of a particular day.
This invention relates to in-store customer traffic analysis and personalized marketing in retail environments. The system uses a network of biometric sensors to detect and recognize users as they interact with displays within a retail store. When a user is detected by a sensor associated with a specific display, the system extracts a user identifier and correlates the interaction with that display. A foot traffic record is generated and stored, linking the user to the display. The system further determines the user's demographic group and retrieves demographic-display data, which indicates how users of that demographic typically interact with the display. Based on this data, the system generates a recommendation and automatically adjusts directional signage on the display to guide the user toward a relevant product. The system also calculates a score based on how long the user spends near the display, associating this score with the user's identifier and approach direction. This data is stored and used to update a broader demographic interest score for the display during specific time slots. The invention aims to enhance customer engagement by dynamically tailoring display content and signage based on real-time user demographics and historical interaction patterns, improving targeted marketing and product visibility in retail settings.
9. The method of claim 8 , wherein the first sensor senses at least one of a face of the first user, a fingerprint of the first user, or a voice of the first user.
This invention relates to user authentication systems, specifically methods for verifying the identity of a user based on biometric data. The problem addressed is the need for secure and reliable authentication in environments where multiple users may interact with a shared device or system. Traditional authentication methods, such as passwords or PINs, are vulnerable to theft or guessing, while physical tokens can be lost or stolen. Biometric authentication offers a more secure alternative by leveraging unique physiological or behavioral characteristics of an individual. The method involves using a first sensor to capture biometric data from a first user, such as facial recognition, fingerprint scanning, or voice recognition. This data is then processed to authenticate the user's identity. The system may also include a second sensor to capture additional biometric data from a second user, allowing for multi-user authentication or verification. The authentication process may involve comparing the captured biometric data against stored templates or using machine learning algorithms to analyze and verify the data in real time. The method ensures that only authorized users can access the system, enhancing security and preventing unauthorized access. This approach is particularly useful in applications where high-security authentication is required, such as financial transactions, secure access control, or personal device unlocking.
10. The method of claim 8 , further comprising: obtaining, by the one or more processors, a first user profile corresponding to the first user; identifying, by the one or more processors, a demographic category based on the first user profile; generating, by the one or more processors, a display-demographic map indicating a correlation between the first display and the demographic category; and storing the display-demographic map.
This invention relates to personalized digital content delivery systems, specifically methods for analyzing and correlating user demographics with displayed content. The problem addressed is the lack of granular demographic insights in digital advertising, where content is often displayed without understanding its appeal to specific demographic groups. The invention improves this by dynamically linking displayed content to user demographic data, enabling more targeted and effective content delivery. The method involves obtaining a user profile for a first user, which includes demographic information such as age, gender, location, or interests. The system identifies a demographic category from this profile, such as "young adults in urban areas." It then generates a display-demographic map, which is a data structure or visualization that correlates the displayed content (e.g., an advertisement or video) with the identified demographic category. This map quantifies how different content performs across demographics, allowing advertisers or content providers to refine their targeting strategies. The map is stored for future reference, enabling long-term analysis of demographic trends and content effectiveness. The system may also compare this data with other user profiles to identify broader patterns or optimize content distribution. The invention enhances digital marketing by providing actionable insights into how different demographic groups engage with content.
11. The method of claim 10 , further comprising; obtaining, by the one or more processors, an inventory listing associated with the retail store; and generating, by the one or more processors, a recommendation for a product contained in the inventory listing, based on the display-demographic map.
The invention relates to a system for analyzing customer behavior in a retail environment to improve product placement and recommendations. The system uses sensors to track customer movements and interactions within a retail store, generating a display-demographic map that correlates product displays with customer demographics. This map helps identify which products are most appealing to specific customer groups based on their observed behavior, such as dwell time, engagement, and purchasing patterns. The system also integrates with the store's inventory management system to access an inventory listing. By analyzing the display-demographic map alongside inventory data, the system generates personalized product recommendations for customers. These recommendations are tailored to the customer's demographic profile and their observed interactions with products in the store. The goal is to optimize product placement, enhance customer experience, and increase sales by ensuring that products are displayed in locations where they are most likely to attract the right audience. The system may also include additional features, such as real-time adjustments to product displays based on live customer behavior data, predictive analytics to forecast future trends, and integration with digital signage to dynamically update promotions. The overall approach aims to bridge the gap between physical retail and data-driven personalization, making shopping experiences more efficient and engaging.
12. The method of claim 11 , wherein the one or more processors are further configured to execute instructions to: providing, by the one or more processors, the recommendation for the product contained in the inventory listing via a graphical user interface.
This invention relates to systems and methods for generating and displaying product recommendations from an inventory listing. The technology addresses the challenge of efficiently suggesting relevant products to users, particularly in e-commerce or inventory management systems, by leveraging computational analysis to identify and present tailored recommendations. The method involves using one or more processors to analyze an inventory listing, which includes details such as product attributes, availability, and user preferences. The processors execute instructions to generate a recommendation for a specific product from the inventory. This recommendation is then presented to the user through a graphical user interface (GUI), ensuring the suggestion is easily accessible and visually integrated into the user's browsing or purchasing experience. The GUI may include interactive elements, such as buttons or links, to facilitate further actions like viewing product details or adding the item to a cart. The system may also incorporate additional features, such as filtering recommendations based on user behavior, historical data, or real-time inventory updates. The goal is to enhance user engagement and streamline decision-making by providing timely, relevant product suggestions. This approach improves the efficiency of inventory management and user experience in digital platforms.
13. The method of claim 12 , wherein the one or more processors are further configured to execute instructions to: obtaining, by the one or more processors, a user input via the graphical user interface in response to providing the recommendation for the product contained in the inventory listing; generating, by the one or more processors, a display instruction based on the obtained user input; and providing, by the one or more processors, the generated display instruction via the graphical user interface.
This invention relates to a system for generating and displaying product recommendations within an inventory management interface. The system addresses the challenge of efficiently suggesting relevant products to users while managing large inventories, ensuring recommendations are both accurate and actionable. The system includes a graphical user interface (GUI) that presents inventory listings and dynamically generates product recommendations based on user interactions. When a recommendation is provided, the system captures user input through the GUI, processes this input to generate display instructions, and updates the interface accordingly. This allows users to interact with recommendations in real-time, improving decision-making and workflow efficiency. The system leverages computational processing to analyze user behavior and inventory data, ensuring recommendations are contextually relevant. The dynamic display updates enhance user engagement by providing immediate feedback and facilitating seamless navigation through inventory listings. This approach optimizes inventory management by reducing manual effort and improving the accuracy of product suggestions.
14. The method of claim 8 , wherein the one or more processors are further configured to execute instructions to: receiving, by the one or more processors, a second sensor signal indicating that a second user has been recognized by the first sensor; extracting, by the one or more processors, a second user identifier from the second sensor signal; correlating, by the one or more processors, the second sensor signal to the first display; generating, by the one or more processors, a second foot traffic record associated with the second user identifier and the first display, based on the second sensor signal; storing the second generated foot traffic record; and generating, by the one or more processors, a recommendation for a product identified in the retail inventory listing based on a display-demographic map, wherein the display-demographic is based on a first user profile related to the first user and a second user profile related to the second user.
This invention relates to a system for tracking user interactions with retail displays and generating product recommendations based on demographic data. The system uses sensors to detect users near a retail display and extracts user identifiers from sensor signals. When a user is recognized, the system correlates the sensor signal to the specific display, generates a foot traffic record associating the user identifier with the display, and stores this record. The system then analyzes the foot traffic data to create a display-demographic map, which links user profiles to specific displays. This map is used to generate product recommendations by identifying products in the retail inventory that align with the demographics of users who interacted with the display. The system enhances retail marketing by providing personalized recommendations based on observed user behavior and demographic insights, improving customer engagement and sales targeting. The invention automates the collection and analysis of foot traffic data, enabling retailers to optimize display placements and tailor promotions to specific customer segments.
15. A non-transitory computer readable medium storing instructions that, when executed by one or more hardware processors, configure the one or more hardware processors to perform operations comprising: receiving, over an electronic communications network, a first sensor signal indicating that a first user has been recognized by a first biometric sensor of a sensor network in a retail store, the first sensor being associated with at least one display in the retail store; extracting a first user identifier from the first sensor signal; correlating the first sensor signal to the at least one display; generating a first foot traffic record associated with the first user identifier and the at least one display, based on the first sensor signal; storing the first foot traffic record; determining a user demographic of the first user; obtaining, based on an identifier of the first display, demographic-display data associated with the determined user demographic, the demographic-display data indicating an interest correlation between the at least one display and the user demographic in a time window; determining a recommendation to be displayed in the at least one display based on the demographic-display data; generating a processor-executable instruction to modify directional signage of the display towards a product in the retail store according to the recommendation in an automated fashion; calculating a first score based on an amount of time the first user spends within a proximity of the modified at least one display and a direction from which the first user approached the display associating the first score with the first user identifier; storing the associated score; and updating, based on the amount of time the first user spends within a proximity of the modified first display, a second score indicating an interest of members of a second user demographic group associated with the first user in the first display in a time slot of a particular day.
This invention relates to a system for analyzing and optimizing retail store displays based on real-time biometric data and user demographics. The system uses a network of biometric sensors to detect and recognize users as they move through a retail store. When a user is recognized by a sensor near a display, the system extracts a user identifier and correlates the user's presence with the specific display. It then generates a foot traffic record linking the user to the display and determines the user's demographic information. Based on this demographic data, the system retrieves demographic-display data that indicates how well the display aligns with the user's demographic interests. The system then generates a recommendation to modify the display's directional signage to highlight a relevant product. The modification is automated, adjusting the display to guide the user toward the product. The system also tracks the user's interaction with the modified display, calculating a score based on the time spent near the display and the approach direction. This score is stored and associated with the user. Additionally, the system updates a broader demographic interest score for the display, reflecting how members of the user's demographic group engage with the display during specific time slots. This data helps optimize future display configurations to better target different demographic groups.
16. The non-transitory computer readable medium of claim 15 , the operations further comprising; receiving a second sensor signal indicating that a second user has been recognized by the first sensor in the retail store; extracting a second user identifier from the second sensor signal; correlating the second sensor signal to the at least one display; generating a second foot traffic record associated with the second user identifier and the at least one display, based on the second sensor signal; storing the second generated foot traffic record; and generating a recommendation for a product identified in the retail inventory listing based on a display-demographic map, wherein the display-demographic map is based on a first user profile related to the first user and a second user profile related to the second user.
This invention relates to a system for tracking foot traffic in a retail store and generating product recommendations based on user demographics. The system uses sensors to detect and recognize users as they interact with displays in the store. When a user is detected, a sensor signal is received, and a user identifier is extracted from the signal. The system then correlates the user's presence with a specific display and generates a foot traffic record linking the user identifier to the display. This data is stored for analysis. The system also generates product recommendations by analyzing a display-demographic map, which is created using user profiles associated with the detected users. The profiles include demographic information, allowing the system to tailor recommendations based on the characteristics of users who interacted with particular displays. The system can track multiple users, updating the display-demographic map as new data is collected, and refine recommendations over time. This approach enhances personalized marketing by leveraging real-time foot traffic data and user demographics to suggest products that align with the preferences of the store's customer base.
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November 10, 2020
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